Damped multichannel singular spectrum analysis for 3D random noise attenuation

Geophysics ◽  
2016 ◽  
Vol 81 (4) ◽  
pp. V261-V270 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang ◽  
Yangkang Chen ◽  
Huijian Li ◽  
Shuwei Gan
Keyword(s):  
Spectrum Analysis ◽  
Seismic Data ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Superior Performance ◽  
Damping Factor ◽  
Signal Subspace ◽  
Noise Attenuation ◽  
Random Noise Attenuation ◽  
Singular Spectrum

Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation in seismic data, which decomposes the vector space of the Hankel matrix of the noisy signal into a signal subspace and a noise subspace by truncated singular value decomposition (TSVD). However, this signal subspace actually still contains residual noise. We have derived a new formula of low-rank reduction, which is more powerful in distinguishing between signal and noise compared with the traditional TSVD. By introducing a damping factor into traditional MSSA to dampen the singular values, we have developed a new algorithm for random noise attenuation. We have named our modified MSSA as damped MSSA. The denoising performance is controlled by the damping factor, and our approach reverts to the traditional MSSA approach when the damping factor is sufficiently large. Application of the damped MSSA algorithm on synthetic and field seismic data demonstrates superior performance compared with the conventional MSSA algorithm.

Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. V385-V396 ◽  
Author(s):  
Mohammad Amir Nazari Siahsar ◽  
Saman Gholtashi ◽  
Amin Roshandel Kahoo ◽  
Wei Chen ◽  
Yangkang Chen
Keyword(s):  
Dictionary Learning ◽  
Seismic Data ◽  
Random Noise ◽  
Data Driven ◽  
Superior Performance ◽  
Noise Attenuation ◽  
3D Seismic ◽  
Random Noise Attenuation ◽  
3D Data ◽  
3D Seismic Data

Representation of a signal in a sparse way is a useful and popular methodology in signal-processing applications. Among several widely used sparse transforms, dictionary learning (DL) algorithms achieve most attention due to their ability in making data-driven nonanalytical (nonfixed) atoms. Various DL methods are well-established in seismic data processing due to the inherent low-rank property of this kind of data. We have introduced a novel data-driven 3D DL algorithm that is extended from the 2D nonnegative DL scheme via the multitasking strategy for random noise attenuation of seismic data. In addition to providing parts-based learning, we exploit nonnegativity constraint to induce sparsity on the data transformation and reduce the space of the solution and, consequently, the computational cost. In 3D data, we consider each slice as a task. Whereas 3D seismic data exhibit high correlation between slices, a multitask learning approach is used to enhance the performance of the method by sharing a common sparse coefficient matrix for the whole related tasks of the data. Basically, in the learning process, each task can help other tasks to learn better and thus a sparser representation is obtained. Furthermore, different from other DL methods that use a limited random number of patches to learn a dictionary, the proposed algorithm can take the whole data information into account with a reasonable time cost and thus can obtain an efficient and effective denoising performance. We have applied the method on synthetic and real 3D data, which demonstrated superior performance in random noise attenuation when compared with state-of-the-art denoising methods such as MSSA, BM4D, and FXY predictive filtering, especially in amplitude and continuity preservation in low signal-to-noise ratio cases and fault zones.

Signal extraction using randomized-order multichannel singular spectrum analysis

Geophysics ◽  
2017 ◽  
Vol 82 (2) ◽  
pp. V69-V84 ◽  
Author(s):  
Weilin Huang ◽  
Runqiu Wang ◽  
Yimin Yuan ◽  
Shuwei Gan ◽  
Yangkang Chen
Keyword(s):  
Spectrum Analysis ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Data Sets ◽  
Noise Attenuation ◽  
Coherent Noise ◽  
Singular Spectrum ◽  
3D Data ◽  
Time Space ◽  
Ground Roll

Multichannel singular spectrum analysis (MSSA) is an effective algorithm for random noise attenuation; however, it cannot be used to suppress coherent noise. This limitation results from the fact that the conventional MSSA method cannot distinguish between useful signals and coherent noise in the singular spectrum. We have developed a randomization operator to disperse the energy of the coherent noise in the time-space domain. Furthermore, we have developed a novel algorithm for the extraction of useful signals, i.e., for simultaneous random and coherent noise attenuation, by introducing a randomization operator into the conventional MSSA algorithm. In this method, which we call randomized-order MSSA, the traces along the trajectory of each signal component are randomly rearranged. Two ways to extract the trajectories of different signal components are investigated. The first is based on picking the extrema of the upper envelopes, a method that is also constrained by local and global gradients. The second is based on dip scanning in local processing windows, also known as the Radon method. The proposed algorithm can be applied in 2D and 3D data sets to extract different coherent signal components or to attenuate ground roll and multiples. Different synthetic and field data examples demonstrate the successful performance of the proposed method.

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